Recent work from Anthropic and others claims that LLMs' chains of thoughts can be “unfaithful”. These papers make an important point: you can't take everything in the CoT at face value. As a result, people often use these results to conclude the CoT is useless for analyzing and monitoring AIs. Here, instead of asking whether the CoT always contains all information relevant to a model's decision-making in all problems, we ask if it contains enough information to allow developers to monitor models in practice. Our experiments suggest that it might.

Sharing details about HCAST (Human-Calibrated Autonomy Software Tasks), a benchmark we’ve been developing to measure the abilities of frontier AI systems to complete diverse software tasks autonomously.